Enhanced passive intermodulation detection in wireless networks

ABSTRACT

Interference caused by passive intermodulation (PIM) can be automatically detected at receivers in a wireless telecommunications network, and the accuracy of PIM detection can be increased by de-weighting or ignoring time slots in which non-PIM interference is detected at a target receiver.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/422,888, filed May 24, 2019, which is a continuation-in-part of U.S.application Ser. No. 16/389,846, filed Apr. 19, 2019, which is acontinuation-in-part of U.S. application Ser. No. 16/179,683, filed Nov.2, 2018, which are incorporated by reference herein.

BACKGROUND

Modern wireless communications systems operate in environments wheresignal quality to and from end-user devices is limited by interferencecoming from diverse sources. A wireless communication system mayexperience unexpected network interference originating from intentionaland/or unintentional RF generating sources. These potential interferencesources include other users served by the same or nearby base stations,industrial machinery, electronic test equipment radiating signals in thebands of interest, undesired mixing products generated by the wirelesscommunication system, and illegal radio sources operating in the wrongfrequency bands. The presence of these interference sources results indegraded system service and reduced wireless network capacity coverageas the intentional system signals suffer degradation due to theseinterferers.

Passive Inter-Modulation (PIM) may occur when one or more signalsencounter a non-linear medium. For a single signal, PIM may generate newsignals (hereinafter called PIM signals) with frequencies that are aninteger multiple of the original frequencies of the signal. For two ormore signals, the PIM may generate PIM signals having frequencies equalto sums of the frequencies of the two or more signals, differences ofthe frequencies of the two or more signals, and combinations thereof. Asa result, the PIM signals may interfere with signals having frequenciesother than the frequencies of the signals that caused the PIM signals tooccur.

Non-linear media that generate PIM include ferromagnetic materials,corroded metal parts, and mis-joined connectors. For example, a rustedmetal strut or a corroded electrical connector may generate PIM signals.Accordingly, the effects of time and exposure to the elements can causePIM to occur and increase in items that when new and properly installeddid not generate significant amounts of PIM.

Traditional methods for PIM detection involve manual on-site inspectionswhich require intentionally disabling transmitting equipment and thegeneration of test signals. Manual inspections require turning off therevenue-generating network equipment and deploying personnel into thefield, often at off-peak hours such as the middle of the night.Accordingly, manual inspections incur high costs and result in serviceinterruptions.

Operators of communication system, especially wireless communicationssystems that operate over wide areas, may monitor their installations todetect new or increased levels of PIM that may cause unacceptable levelsof interference. Conventional approaches to detecting PIM involvedeploying technicians to base station sites to perform manual testing asdescribed above, which is expensive, time consuming, and causes serviceinterruptions. To reduce the cost of operating the communication systemand improve its reliability, it would be advantageous to detect theexistence of new or increased levels of PIM: 1) automatically, 2)without interfering with or interrupting the operation of thecommunication system, 3) without specialized signal generationequipment, and 4) without having technicians present at theinstallations where the interference caused by PIM is being received.

Interference caused by PIM affects frequencies that are different fromfrequencies that trigger the PIM interference, and in some cases aportion of the frequencies affected by PIM interference are used by aco-sited cell. In some installations, co-sited cells are operated bydifferent operators which are generally market competitors, so thattransmissions from one operator can cause interference that affects adifferent operator. Multiple types of interference affect frequenciesand times that are used by different operators. In addition, non-PIMexternal interference can compromise the accuracy of PIM detection.

TECHNICAL FIELD

The present disclosure relates to determining a system and method forexternal interference in a wireless telecommunications network. Specificembodiments relate to detecting and resolving PIM interference,determining external interference that is not PIM interference, andimproving the accuracy of PIM interference detection by accounting forthe external interference that is not PIM.

BRIEF SUMMARY

This disclosure describes automated processes for detecting PIMinterference in a wireless network. A system and method according toembodiments determine time slots during which external interference thatis not caused by PIM is affecting a target receiver, and adapt PIMinterference detection parameters accordingly. The non-PIM interferencemay be detected at the target receiver alone, or may be detected bydetermining that characteristics of the interference are correlatedbetween multiple receivers separated by distance.

This disclosure describes methods of securely sharing informationbetween automated interference detection tools such that information tosupport accurate detection of PIM interference is exchanged, while anoperator's proprietary information is not publicly visible tocompetitive network operators. Some embodiments are directed to anautomated system for detecting external interference at shared cellsites that utilizes unique data encryption or data obfuscation keys toindividual operators and an interference detection system that is ableto decrypt and utilize each supplied data stream to detect andcharacterize external interference in a wireless network.

This disclosure describes techniques to detect the generation of PassiveInter-Modulation (PIM)-caused interference in or near a wirelessreceiver without the use of special-purpose test signals. In particular,embodiments include a system that receives signals in a frequency bandand performs analysis on the received signals to detect PIM signalsgenerated by two or more transmissions from nearby orshared-infrastructure wireless transmitters in the ordinary course oftheir operation. Embodiments may be applied to a wireless communicationsystem to detect PIM-generating conditions without having to interruptthe normal operation of the wireless communication system, withoutreconfiguring the wireless communication system, and remotely—that is,without the presence of technicians at or near the site where the PIM isdetected. As a result, embodiments reduce the cost and increase thespeed of detecting PIM in the presence of PIM-generating conditions.

In an embodiment, a method for remote detection of interferencegenerated by Passive Inter-Modulation (PIM) comprises: determiningintermodulation product information for a plurality of transmitters,receiving downlink power information of the plurality of transmitters,determining, using the intermodulation product information and thedownlink power information, a Weighted Downlink Power (WDP) signal foran intermodulation product, and determining, using the WDP signal, a PIMDetection Assessment (PIMDA) score of the intermodulation product,wherein a value of the PIMDA score corresponds to interference generatedby PIM.

In an embodiment, a wireless telecommunications system comprises areceiver and a processor. The wireless telecommunications system isconfigured to perform, using the processor: determining intermodulationproduct information for a plurality of transmitters, receiving downlinkpower information of the plurality of transmitters, determining, usingthe intermodulation product information and the downlink powerinformation, a Weighted Downlink Power (WDP) signal for anintermodulation product, and determining, using the WDP signal, a PIMDetection Assessment (PIMDA) score of the intermodulation product,wherein a value of the PIMDA score corresponds to interference generatedby PIM.

Embodiments of the present disclosure include a non-transitorycomputer-readable medium with computer-executable instructions storedthereon which, when executed by a processor, performs one or more of thesteps described above.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an installation of a wireless communication systemaccording to an embodiment.

FIG. 2 illustrates the generation of interference by PassiveInter-Modulation (PIM).

FIG. 3 includes a table showing mixing products for order 2 and 3 fortwo single toned signals.

FIG. 4 includes a table of illustrative values of a bandwidth pre-factorδ_(n) for determining an effective bandwidth of a PIM-caused signal,according to an embodiment.

FIG. 5 illustrates a relationship between expected PIM-causedinterference and measured PIM-caused interference in an embodiment.

FIG. 6 includes a table summarizing information stored in a database foreach of one or more intermodulation products I_(n), according to anembodiment.

FIG. 7 illustrates a process for detecting PIM-caused interference at afocus cell, according to an embodiment.

FIG. 8 illustrates a process for performing a PIM assessment, accordingto an embodiment.

FIG. 9 illustrates process for calculating a PIM Detection Assessment(PIMDA) score according to an embodiment.

FIG. 10 includes a table showing illustrative thresholds used indetermining a PIMDA score, according to an embodiment.

FIG. 11 includes a table showing illustrative feature weights for use indetermining a PIMDA score, according to an embodiment.

FIG. 12 illustrates a system for detection of PIM-caused interference,according to an embodiment.

FIG. 13 illustrates a networked PIM-caused interference detection systemaccording to an embodiment.

FIG. 14 illustrates PIM-caused interference that occurs in two separatenetworks.

FIG. 15 illustrates an embodiment of a process for detecting externalinterference using data from at least two operators.

FIG. 16 illustrates a first embodiment of a system for using data frommultiple network operators to determine the presence of externalinterference in a wireless communications network.

FIG. 17 illustrates a second embodiment of a system for using data frommultiple network operators to determine the presence of externalinterference in a wireless communications network.

FIG. 18 illustrates an embodiment of a process for configuring a PIMdetection system.

FIG. 19 illustrates an embodiment of performing PIM detection in view ofcorrelated interference.

FIG. 20 illustrates an embodiment of performing PIM detection in view ofnon-PIM characteristics of a target receiver.

DETAILED DESCRIPTION

A detailed description of embodiments is provided below along withaccompanying figures. The scope of this disclosure is limited only bythe claims and encompasses numerous alternatives, modifications, andequivalents. Although steps of various processes are presented in aparticular order, embodiments are not necessarily limited to beingperformed in the listed order. In some embodiments, certain operationsmay be performed simultaneously, in an order other than the describedorder, or not performed at all.

Numerous specific details are set forth in the following description inorder to provide a thorough understanding. These details are providedfor the purpose of example and embodiments may be practiced according tothe claims without some or all of these specific details. For the sakeof clarity, technical material that is known in the technical fieldsrelated to this disclosure has not been described in detail so that thedisclosure is not unnecessarily obscured.

Embodiments of the present disclosure represent a number of improvementsto existing wireless communication technology. Embodiments allowdetection of PIM-caused interference without the use of special testequipment. Embodiments allow detection of PIM-caused interference atgeographically remote sites without the need to send technicians tothose sites. Embodiments allow detection of PIM-caused interferencewithout the need to take the components being subjected to thePIM-caused interference out of active service. Embodiments allowdetection of PIM-caused interference inexpensively and on a regularschedule without disrupting the operation of the communication system inwhich the detection is being performed.

Embodiments of the present disclosure are especially well suited todetecting interference caused by PIM remotely in wirelesstelecommunication systems that are spread out over a large geographicalarea, where dispatching technicians to remote and at times difficult toaccess sites to perform PIM interference detection would be expensive orimpractical, and where taking system components out of service toperform PIM interference testing might result in a loss of service inareas covered by the system.

FIG. 1 illustrates a wireless network installation (hereinafterinstallation) 10 according to an embodiment. The installation 10includes an equipment locker 100 and a tower 110. The installation maycomprise a Base Transceiver Station (BTS), Evolved Node B (eNodeB), orthe like.

The equipment locker 100 includes first, second, and third controlsystems (CSs) 102A, 102B, and 102C. The first CS 102A transmits signalsusing a first power amplifier (PA) 104A and receives signals using a LowNoise Amplifier (LNA) 108. The second CS 102B transmits signals usingsecond and third PAs 104B and 104C, and may also receive signals usingone or more LNAs (not shown). The third CS 102C transmits signals usinga fourth PA 104D, and may also receive signals using one or more LNAs(not shown). Frequencies transmitted using each of the first throughfourth PAs may differ from each other, and may also differ from thefrequencies received using the LNA 108.

The tower 110 includes first and second transmit antennas (TXANTs) 116Aand 116B and a receive antenna (RXANT) 118. The antennas may be fixed tothe tower 110 using struts 120 or similar structural elements. In theinstallation 10 illustrated in FIG. 1, a corroded area 120 c is presenton one of the structural elements of the tower 110.

Outputs of the first to third PAs 104A to 104C are combined by an RFCombiner 106 and delivered to the first TXANT 116A using a first coaxialcable 112A that includes a first connector 114A. The output of thefourth PA 104D is delivered to the second TXANT 116B using a thirdcoaxial cable 112C. Signals received from user equipment (UE) 124through the RXANT 118 are delivered to the LNA 108 through a secondcoaxial cable 112B that includes a second connector 114B.

The corroded area 120 c, the first connector 114A, and the secondconnector 114B are each potential sources of PIM. For example, ifcorrosion or contact between metals with different galvanic potentialsare present in the first connector 114A, PIM signals may be created bysignals transmitted using each of the first to third PAs 104A to 104Cand by combinations thereof.

If corrosion or contact between metals with different galvanicpotentials are present in the second connector 114B, PIM signals may becreated by first signal 132A transmitted by the first TXANT 116A andsecond signal 134A transmitted by the second TXANT 116B after eachsignal is received by the RXANT 118. If corroded area 120 c is present,PIM signals 136 may be created by third signal 132B transmitted by thefirst TXANT 116A and fourth signal 134B transmitted by the second TXANT116B, and the PIM signals 136 may be received by the RXANT 118.

One or more of the second through fourth PAs 104B to 104D may betransmitting at the same time as signals 130 are being received by thefirst CS 102A from the UE 124 through the LNA 108. The signalstransmitted by the one or more of the second through fourth PAs 104B to104D may have different frequencies than the frequencies of the signals130 being received from the UE 124. Accordingly, in the absence ofintermodulation, the simultaneously-transmitted signals of the secondthrough fourth PAs 104B to 104D would not interfere with the signals130. However, the PIM signals described above may interfere withreception of the signals 130.

The equipment configurations shown as being included in the equipmentlocker 100 and on the tower 110 may correspond to one sector ofmulti-sector wireless cell, and the equipment locker 100 and tower 110may each include additional equipment configurations, similar to theconfiguration shown in FIG. 1, that respectively correspond to one ormore other sectors of the wireless cell. Transmissions from each sectormay generate PIM-caused interference in each of the other sectors of thewireless cell.

FIG. 1 also shows a performance monitoring (PM) system 140 incommunication with the CSs 102A, 102B, and 102C. The PM system 140 maybe located remotely from the installation 10, and may communicated withcomponents of the installation 10 over a Wide-Area Network (WAN), abackhaul network, the Internet, or the like.

Each of the PM system 140 and the CSs 102A, 102B, and 102C may includerespective processors, respective memories, and respective input andoutput devices. Embodiments of the present disclosure may beincorporated in whole or in part into the PM system 140, or one or moreof the CSs 102A, 102B, and 102C, or both.

FIG. 2 illustrates the generation of interference by PassiveInter-Modulation (PIM). FIG. 2 shows bandwidths of a first downlink (DL)signal DL1 having a first DL center frequency Df₁ and a bandwidth of Δf,a second DL signal DL2 having a second DL center frequency Df₂ and abandwidth of Δf, a first uplink (UL) channel UL1 having a first ULcenter frequency Uf₁ and a bandwidth of Δf, and second UL channel UL2having a second UL center frequency Uf₂ and a bandwidth of Δf. AlthoughFIG. 2 shows an example where all of the UL channels and DL signals havea same bandwidth of Δf, embodiments are not limited thereto.

The first DL signal DL1, second DL signal DL2, first UL channel UL1, andsecond UL channel UL2 may each be signals or channels of respectivecells of one or more wireless communication networks. The cells mayoperate independently, and may use different or shared antennas on asame tower or on towers that are near each other (e.g., the cells may beco-sited).

FIG. 2 also illustrates some of the PIM signals that may be generatedfrom the interactions of one or more of the first and second DL signalsDL1 and DL2 with a non-linear medium. A first PIM signal PIM1 isgenerated at a center frequency DF₂−DF₁ equal to the difference betweenthe center frequency DF₂ of the second DL signal DL2 and the centerfrequency DF₁ of the first DL signal DL1. A second PIM signal PIM2 isgenerated at a center frequency 2·DF₁ equal to twice the centerfrequency DF₁ of the first DL signal DL1. A third PIM signal isgenerated at a center frequency DF₂+DF₁ equal to the sum of the centerfrequency DF₂ of the second DL signal DL2 and the center frequency DF₁of the first DL signal DL1. A fourth PIM signal PIM2 is generated at acenter frequency 2·DF₂ equal to twice the center frequency DF₂ of thesecond DL signal DL2.

The bandwidth of each PIM signal is equal to a sum of the bandwidths ofthe signals that caused the PIM signal to be generated. For example,because the first PIM signal PIM1 is generated from the mixing of thefirst and second DL signals DL1 and DL2 in the non-linear medium, thebandwidth of the first PIM signal PIM1 is equal to 2·Δf. Similarly,because the second PIM signal PIM2 is generated from the mixing of thefirst DL signals DL1 with itself, the bandwidth of the second PIM signalPIM2 is also equal to 2·Δf.

Not shown in FIG. 2 are PIM signals generated at center frequencies ofDF₁−DF₁ and DF₂−DF₂, which would each have a bandwidth of Δf starting at0 Hz (DC). Such PIM signals are unlikely to interfere with uplinktransmissions.

As shown in FIG. 2, the frequencies occupied by the first and second PIMsignals PIM1 and PIM2 overlap with the frequencies occupied by the firstand second UL channels UL1 and UL2, respectively. As a result, the firstand second PIM signals PIM1 and PIM2 may interfere with the first andsecond UL channels UL1 and UL2, respectively.

FIG. 2 illustrates PIM signals generated by a quadratic mixing process,that is, a nonlinearity of order 2. For two signals S₁(t)=A₁·cos(2πf₁·t)and S₂(t)=A₂·cos(2πf₂·t), a mixing with nonlinearity of order 2transfers energy to frequencies of the form f=a₁f₁+a₂f₂ for positive andnegative integer values of a₁ and a₂ satisfying |a₁|+|a₂|=2.

FIG. 3 includes a Table 1 showing mixing products for order 2 and 3non-linearities for two single-toned signals. As shown by Table 1,non-linearities of higher order produce a larger number of spurioussignals.

Two properties of PIM signals can be seen in Table 1: First, it ispossible to list all intermodulation combinations of a givennonlinearity order using simple combinatorial analysis. Second, given anonlinearity of order n, the amplitude A_(IM) of a particularintermodulation product of the form a₁f₁+a₂f₂, where |a₁|+|a₂|=n,satisfies:A _(IM) ∝A ₁ ^(|a) ¹ ^(|) A ₂ ^(|a) ² ^(|)  Equation 1where ∝ indicates “proportional to.” For example, the amplitude of themixing product corresponding to the combination f₁−f₂ produced by anonlinearity of order 2 is proportional to A₁A₂ (see the correspondingentry in Table 1), reflecting the dependence of the intermodulationenergy on the combinatorial weights a₁=1 and a₂=−1.

For real-valued, band-limited signals such as signals that are used inwireless communication cellular networks, e.g., signals S₁ and S₂centered at frequencies f₁ and f₂ and having finite bandwidths Δf₁ andΔf₂, respectively, the spectral representation is given by:

$\begin{matrix}{{{{\hat{S_{1}}(f)} = {{{A_{1}\left( {f - f_{1}} \right)}e^{i\; 2{\pi{({f - f_{1}})}}t}} + {{\overset{\_}{A_{1}}\left( {f - f_{1}} \right)}e^{{- i}\; 2{\pi{({f - f_{1}})}}t}}}},{{- \frac{\Delta\; f_{1}}{2}} \leq f \leq {+ \frac{\Delta\; f_{1}}{2}}}}{{{\hat{S_{2}}(f)} = {{{A_{2}\left( {f - f_{2}} \right)}e^{i\; 2{\pi{({f - f_{2}})}}t}} + {{\overset{\_}{A_{2}}\left( {f - f_{2}} \right)}e^{{- i}\; 2{\pi{({f - f_{2}})}}t}}}},{{- \frac{\Delta\; f_{2}}{2}} \leq f \leq {+ \frac{\Delta\; f_{2}}{2}}}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$where A₁(f) and A₂(f) are complex-valued functions with compact support,i.e., A_(i)(f)≡0 for |f|>Δf_(i)/2, and A_(i) denotes the complexconjugate of A_(i). When band-limited signals S₁ and S₂ are mixedthrough a quadratic process, i.e. a nonlinearity of order 2, PIM-causedband-limited signals centered at frequencies corresponding tointermodulation products (also known as intermodulation mixing products)are generated, as illustrated in FIG. 2, wherein the case Δf1=Δf2=Δf isconsidered. The effective PIM bandwidth Δf_(PIM) of these PIM-causedsignals is:Δf _(PIM)=δ_(n)(Δf ₁ +Δf ₂)  Equation 3where the effective bandwidth pre-factor δ_(n) is chosen so that 99% ofthe energy of the PIM-caused signal is contained within the effectivePIM bandwidth. Illustrative values of the effective bandwidth pre-factorδ_(n) for orders 2 through 7 are shown in Table 2 in FIG. 4.

PIM-caused interference may occur when the spectral density of aPIM-caused signal overlaps with an uplink channel operating in thevicinity of the area where the PIM-caused signal was generated. Forexample, as shown in FIG. 2, communications being performed using thefirst UL channel UL1 is being affected by the PIM signal PIM1corresponding to DL2−DL1, and communications being performed using thesecond UL channel UL2 is being affected by the PIM signal PIM2corresponding to 2·DL1. From the relation between the intermodulationamplitudes and the mixing coefficients a₁ and a₂, the power of theintermodulation product and thus the interference power P_(interf)generated by a PIM signal will satisfy:P _(interf) ∝P ₁ ^(|a) ¹ ^(|) P ₂ ^(|a) ² ^(|)  Equation 4where P₁ and P₂ are the respective transmission powers of the first andsecond DL signals DL1 and DL2. In the simple scenario shown in FIG. 2,the amount of interference power affecting first UL channel UL1 will beproportional to P₁·P₂, while the amount of interference affecting thesecond UL channel UL2 will be proportional to P₁ ².

Embodiments of the PIM interference detection processes disclosed hereinrely on this fundamental relation between the uplink interference causedby PIM and the transmission power of cells contributing to the formationof the PIM-caused signals to assess the likelihood of uplinkinterference measurements at a cell being caused by PIM processes. Sincewireless communications equipment typically reports values associatedwith uplink interference power in logarithmic units, e.g., dBm, therelationship of Equation 4, above, may be rewritten as:log₁₀(P _(interf))=C+|a ₁|log₁₀(P ₁)+|a ₂|log₁₀(P ₂)  Equation 5where the offset C expresses the proportionality of the relation.Knowledge of the actual value of C is not relevant for the analysis orfor the operation of the PIM interference detection process. Based onEquation 5, a Weighted Downlink Power (WDP) signal may be constructedas:WDM=|a ₁|log₁₀(P ₁)+|a ₂|log₁₀(P ₂)  Equation 6which may be considered an “expected signature of PIM-causedinterference.” When PIM is the only source of interference detected by acell for an UL signal, any Uplink Interference Metric (UIM) signal forthe cell defined over some finite time interval should display abehavior that mimics the relation UIM(t)=WDP(t) at least for some rangeof values of interference measurements. That is, if PIM-caused signalsare the only source of interference, the UIM will vary over time in thesame manner as the WDP does. An example UIM signal is a weighted averageof the pmRadioRecInterferencePwr counter (corresponding to a measureduplink noise and interference power on a Physical UL Shared Channel(PUSCH)) for Ericsson equipment over a 24-hour period.

In practice, interference measurements are lower-bounded by the dynamicrange of the measurement equipment, effectively establishing aninterference floor UIM₀. As a result, a more realistic relation betweenthe PIM interference expressed in the UIM and the PIM interferenceexpectation represented by the WDP is UIM(t)=max(UIM₀, WDP(t)), asillustrated in FIG. 5. This relationship between expected PIMinterference (WDP) and measured PIM interference (UIM) provides acondition for PIM detection: if the UIM of a given cell does not satisfythe fundamental relation with the WDP signal, then the interferenceshould not be attributed to PIM. The methodology described in thisdocument relies on the (WDP, UIM) relation to estimate the likelihood ofthe detected interference being caused by PIM.

FIG. 7 illustrates a process 700 for detecting PIM-caused interference(intermodulation products) at a focus cell C_(f), according to anembodiment. The process 700 may be performed by, for example, a controlsystem (CS) located at site of the focus cell C_(f), such as the firstCS 102A of FIG. 1, or by a PM system remote from the site of the focuscell C_(f), such as the PM system 140 of FIG. 1, but embodiments are notlimited thereto. In an embodiment, the focus cell C_(f) may be onesector of a multi-sector cell.

The process 700 includes an initialization phase and an operationalphase. The initialization phase may be performed at installation orstartup of the focus cell C_(f), or whenever a change in local radioenvironment (such as an installation of a new co-sited transmitter, aremoval of an existing co-sited transmitter, a change in the operationof a co-sited transmitter, or the like) occurs. The operational phasemay be performed periodically (for example, once a day) to detectinterference caused by PIM.

At S702 of the initialization phase, the process 700 performs an initialinventory of transmitters that are near a focus cell C_(f). Transmittersnear the cell may include transmitters co-sited with (e.g., transmittingfrom the same tower or physical support as) the focus cell C_(f). Theinventory includes the transmission frequencies used by the nearbytransmitters and the power levels used to transmit in each frequency.The transmitters may include transmitters operated by the same entity asthe focus cell C_(f) (such as 3G transmitters operated by the operatorof a 4G focus cell or transmitters in a different sector of the basestation) and transmitters operated by different entities than theoperator of the focus cell C_(f).

At S704 of the initialization phase, the process 700 determinespotential intermodulation products that can be generated by PIM ofsignals from the nearby (that is, inventoried) transmitters. Inparticular, the intermodulation products that may interfere with uplinktransmission to the focus cell C_(f) are determined and the degree towhich those intermodulation products might impact the uplinktransmission is assessed.

Consider a communication system consisting of M co-sited cells C^((i))having uplink and downlink channels centered at frequencies f_(UL)^((i)) and f_(DL) ^((i)), respectively, and having bandwidths Δf^((i))for all i=1 . . . M (it is common industry practice to operate both theuplink and downlink channels with the same bandwidth). For the sake ofgenerality it is assumed that f_(UL) ^((i))≠f_(DL) ^((i)), i.e. allcells operate in the Frequency-Division Duplex (FDD) mode. The case forcells operating in the Time-Division Duplex (TDD) mode will be treatedas a special case, and its discussion is deferred until later in thisdocument. The process for generation of intermodulation products assumesthat f_(UL) ^((i))≠f_(DL) ^((i)) for i≠j, i.e., only one cell istransmitting on each downlink channel. The case for multiple cells usingthe same downlink channel may be handled by maintaining a list of cellsper downlink channel.

For the simplified communication system described above, anintermodulation product of order n is defined as the pair I_(n)=(f₁,Δf₁), where:

$\begin{matrix}{{{f_{I} = {\sum\limits_{k = 1}^{M}\;{\alpha_{k}f_{DL}^{(k)}}}},{{\Delta\; f_{I}} = {\delta_{n}{\sum\limits_{k = 1}^{M}\;{{\alpha_{k}}\Delta\; f^{(k)}}}}}}{{\alpha_{k} \in {{\mathbb{Z}}{\forall{k\mspace{14mu}{and}\mspace{14mu}{\sum\limits_{k = 1}^{M}\;{\alpha_{k}}}}}}} = n}} & {{Equation}\mspace{14mu} 7}\end{matrix}$where f₁ is the center frequency of the interference signal, Δf₁ is thebandwidth of the interference signal, and δ_(n) is the effectivebandwidth pre-factor from Table 2. Given a focus cell C_(f) with uplinkchannel UL_(f), the set IM_(n)(C_(f)) of intermodulation products oforder n which potentially contribute to uplink interference at C_(f) isdefined as:IM_(n)(C _(f))={I _(n)=(f ₁ ,Δf ₁): I _(n) ∩UL _(f)≠∅}  Equation 8that is, IM_(n)(C_(f)) is the set of intermodulation products whosefrequencies have a non-null intersection with the uplink channel. Inaddition to the center frequency and bandwidth associated with eachI_(n) in IM_(n)(C_(f)), the parameter κ_(UL) corresponding to thepercentage of the uplink channel UL_(f), that intersects theintermodulation product I_(n) is computed, e.g.:κ_(UL)=Δ(I_(n)∩UL_(f))/ΔUL_(f), where Δ(I_(n)∩UL_(f)) is the bandwidthof the intersection of I_(n) and UL_(f), and ΔUL_(f) is the bandwidth ofthe uplink channel UL_(f).

The sets IM_(n)(C_(f)) are generated for each applicable order n ofnonlinear interference and all of the inventoried transmitters, and arethen stored in a long-term retention database for later use, as theydepend on network configuration parameters which change only rarely overlong periods of time. For example, in an embodiment, sets IM₂(C_(f)),IM₃(C_(f)), IM₄(C_(f)), and IM₅(C_(f)) may be generated for nonlinearinterference of orders 2, 3, 4, and 5, respectively. The informationstored for each intermodulation product In is summarized in Table 3 ofFIG. 6.

Sets IM_(n)(C_(x)) may be generated for one or more other cells asdescribed for the focus sell C_(f), and these sets may also be stored inthe long-term retention database.

Cells operating in the Time Division Duplexing (TDD) mode are consideredas special cases at the intermodulation products generation stage ofS704. Given that at any particular time a TDD cell can be in eithertransmission or reception mode, but not both, and that it is standardindustry practice to synchronize all TDD cells sharing a frequency bandin a region, i.e. all co-sited TDD cells operating on the same frequencyband switch between transmission and reception modes in synchrony, thefollowing principle is applied during the computation of intermodulationproducts:

-   -   A TDD cell cannot contribute to PIM for any co-sited TDD cell in        the same frequency band.        According to this principle, the set of overlapping        intermodulation products IM_(n)(C_(f)) of a focus cell C_(f)        using TDD does not includes mixing products IM which have        contributions from TDD cells operating on the same frequency        band as the focus cell C_(f).

Turning to the operational phase, at S706 the process 700 performs adata acquisition stage. Given the focus cell C_(f), the data acquisitionstage retrieves the following information from the corresponding datasource(s):

-   -   1. An Uplink Interference Metric (UIM) signal for the focus cell        C_(f) (e.g., the weighted average of the        pmRadioRecInterferencePwr counter for LTE cells using Ericsson        equipment). Potential data sources for UIM signals may include        one or more short-term retention databases, an Operations        Support System (OSS) directly, or both.    -   2. A Downlink Power Metric (DLPwr(i)) signal for each cell C_(i)        co-sited with C_(f). For example, for cells using Ericsson        equipment, the DLPwr(i) may be the weighted average of the        pmTransmittedCarrierPower counter. Potential data sources for        DLPwr(i) signals are the same as for the UIM signals.    -   3. The sets of intermodulation products IM_(n)(C_(f)) for all        nonlinearity orders of interest (in most cases, nonlinearities        of order up to 5 are sufficient for PIM analysis). For each        nonlinearity order n, the set IM_(n)(C_(f)) consists of all the        mixing products In which have a non-zero overlap with the uplink        channel of the focus cell C_(f). The data source for        IM_(n)(C_(f)) is the long-term retention database described        above with reference to Table 3 of FIG. 6.

The information described above is determined using samples collected ona periodic basis. For example, the system may collect the UIM signal forthe focus cell C_(f) and the DLPwr(i) of the co-sited cells C_(i) every15 minutes (i.e., 96 times a day). Each of the UIM signal and theDLPwr(i) signals may include a respective plurality of valuescorresponding to a period of time. The period of time may include aplurality of timestamps, and each value of the UIM signal and each valueof the DLPwr(i) signals may correspond to one of the plurality oftimestamps.

At S708, the process 700 determines a set of Weighted DL Power (WDP_(i))signals from the individual downlink power signals of the inventoriednearby (e.g., co-sited) cells.

For each nonlinearity order n being considered (e.g., for each of n=2,3, 4, and 5) for the focus cell C_(f) and the data sets {IM_(n)(C_(f)),DLPwr(1), . . . , DLPwr(M)} described above, an intermediate set ofsignals associated with the downlink power of the co-sited cells ofC_(f) is generated as follows:

Sub-step 1a: For each intermodulation product I in IM_(n)(C_(f)),compute the Aggregated Downlink Power AggDP signals (one for eachcontributing downlink frequency):

-   -   Identify all different downlink frequencies available in the        site, and the corresponding cells associated with each        frequency.    -   For each downlink frequency, add the DLPwr(k) signals of all        cells associated with the downlink frequency to determine a        k^(th) Aggregated Downlink Power AggDP_(k) signal. In an        embodiment, the DLPwr(k) for k=1, . . . , M are in linear scales        and normalized to be in the interval [0,1], in order to avoid        signals with large dynamic ranges overpowering other signals.    -   In an embodiment, if DLPwr(k) is unavailable from the data        repository for some k, assume a value of 1.0 for all time stamps        under consideration (Full Transmission Power Assumption). In        other embodiments, historical averages/trends of DLPwr for a        source cell or the site average of available downlink power        signals may be substituted for missing DLPwr of the source cell.

Sub-step 1b: For each intermodulation product I in IM_(n)(C_(f)),compute the Weighted Downlink Power signal WDP_(I) associated to thegiven intermodulation product I:

$\begin{matrix}{{WDP}_{I} = {\frac{1}{I \cdot {order}}{\sum\limits_{\underset{{source}\mspace{14mu}{{freq}.}}{k \in}}{{\alpha_{k}}\mspace{14mu}{\log_{10}\left( {AggDP}_{k} \right)}}}}} & {{Equation}\mspace{14mu} 9}\end{matrix}$where α_(k) is the multiplier corresponding to each harmonic involved inthe definition of the intermodulation product I. The pre-factor1/I.order is included in the computation of WDP_(I) to reflect thedependence of a power level of PIM interference on the order of thenonlinearity causing it, which effect is to change the slope of thefundamental relation (WDP, UIM) from m=1 (as shown in FIG. 5) tom=I.order.

Sub-step 2. Using the set {WD_(I)} of weighted downlink power signals,compute the Averaged Weighted Downlink Power signal AvgWDP as:

$\begin{matrix}{{AvgWDP} = {\sum\limits_{I \in {{IM}_{n}{(C_{f})}}}{\beta_{I}{WDP}_{I}}}} & {{Equation}\mspace{14mu} 10} \\{\beta_{I} = {{I \cdot {overlap}}\text{/}{\sum\limits_{I_{j} \in {{IM}_{n}{(C_{f})}}}{I_{j} \cdot {overlap}}}}} & {{Equation}\mspace{14mu} 11}\end{matrix}$Wherein I.overlap indicates a percentage of the uplink bandwidthoverlapped by the intermodulation product I, and β_(I) corresponds toI.overlap divided by a sum of all the overlap I_(j).overlap of all theintermodulation products that interfere with the focus cell C_(f). Aftersub-steps 1 and 2, the total number of WDP signals should belength(IM_(n)(C_(f)))+1: one for each intermodulation product I plus theaveraged WDP from sub-step 2. In addition to the UIM signal, the set ofWDP signals will be used to generate the set of linear models used bythe process 700 to detect PIM-caused interference.

At S710, the process 700 constructs a set of piece-wise linear models(one linear model for each WDP_(I) signal) for each of the data sets{WDP_(I), UIM}. The piece-wise linear models may be constructed usingregression analysis.

A configuration parameter, ConfigParam.NumOfSegments, is required forthe linear model analysis stage. The parameter specifies the number ofpartitions for each of the intervals [min(WDP_(k)), max(WDP_(k))]associated to each weighted downlink power functions WDP_(k) describedabove. These partitions are used in the computation of local linearmodels as described below.

For each available WDP_(k) signal (one for each I plus one for AvgWDP asdescribed above), the process 700 produces one global linear model andseveral local linear models.

The process 700 produces the global linear model by finding theparameters (m_(G), b_(G)) of the linear model ŷ=m_(G){circumflex over(x)}+b_(G) that fits the data set (WDP_(k), UIM) in the least-squaressense, and compute the average global dispersion/spread σ_(G) of thedata points from the model. The process 700 then computes the globalcorrelation ρ_(G) between the WDP_(k) and UIM signals and computes theminimum and maximum values of the UIM signal.

The process 700 produces the local models by segmenting the data set(WDP_(k), UIM) into data subsets (WDP_(k)(j), UIM(j)), j=1 . . .ConfigParam.NumOfSegments, by splitting the range of values of WDP_(k)into ConfigParam.NumOfSegments segments. Thus, in an illustrativeexample having N segments wherein each segment corresponds to arespective portion of the distribution of the expected power values, WDPvalues in a first Nth percentile (0 to 1/N) (i.e., the lowest WDPvalues) and their respective UIM values are placed in a first segment,WDP values in a second Nth percentile (1/N to 2/N) and their respectiveUIM values are placed in a second segment, and so on, with the highestWDP values and their respective UIM values being placed in the Nthsegment.

For each segment, the process 700 computes the parameters (m(j), b(j))of the linear model ŷ=m_(j){circumflex over (x)}+b_(j) that fits thedata subset (WDP_(k)(j), UIM(j)) in the least-squares sense, computesthe average local dispersion/spread σ(j) of the data points from thelocal model, computes the local correlation ρ(j) between the WDP_(k)(j)and UIM(j) signals, and computes the minimum and maximum values of theUIM(j) signal over the (j)-th segment.

Accordingly, at S710 the process 700 produces length(IM(C_(f)))+1 globallinear models and ConfigParam.NumOfSegments×length(IM_(n)(C_(f)))+1local linear models. For example, if there are 6 intermodulationproducts that may interfere with uplink transmissions of the focus cellC_(f), and 8 segments are used, then 7 global linear models and 56 locallinear models are produced: a global linear model for each WDP_(I)(where I corresponds to an intermodulation product), a global linearmodel for the AvgWDP, a local linear model for each segment for eachWDP_(I), and a local linear model for each segment for the Avg WDP.

At S712, the process 700 performs PIM Detection Assessment (PIMDA) todetect the presence of PIM-caused interference. The process determines aPIMDA score ρ corresponding to a likelihood that the observed uplinkinterference is attributable to PIM. The process 700 may report thePIMDA score ρ to an operator of the focus cell C_(f), who may thendecide whether remedial action is appropriate to address thePIN-generated interference.

In an embodiment, the process 700 reports the PIMDA score ρ to theoperator when the PIMDA score ρ is higher than a predeterminedthreshold. In an embodiment, the process 700 reports the PIMDA score ρwhen it is substantially higher (e.g., increases by more than apredetermined percentage, such as 50%) than a previous PIMDA score ofthe focus cell C_(f).

In an embodiment, the process 700 reports the PIMDA score ρ to theoperator according to a comparison of the PIMDA score ρ to one or morerespective PIMDA scores of one or more co-sited cells of the focus cell.

FIG. 8 illustrates a process 812 for performing a PIM DetectionAssessment (PIMDA) according to an embodiment. The process 812 may beperformed as part of S712 of process 700 of FIG. 7.

At S822, the process 812 divides the data into data segments, each datasegment corresponding to one of the local linear models determined inS710 of process 700.

At S824, the process 812 determines for each of the data segmentswhether the data segment is a high traffic segment or a low trafficsegment. The identification of traffic levels in each segment may bemade using, for example, downlink power DLPwr data for each segment. Inan illustrative embodiment, ⅔rds of the segments are determined to below traffic segments, and ⅓rd of the data segments are determined to behigh traffic segments, according to the percentile-based orderingdescribed above with respect to process 700.

At S826, the process 812 determines an Average PIMDA score ρ_(avg) usingall the linear models (local and global) corresponding to the data setcomprising the Averaged Weighted Downlink Power signal AvgWDP signal andthe UIM signal (AvgWDP, UIM). The Average PIMDA score ρ_(avg) may bedetermined as described with respect to FIG. 9, below.

At S828, the process 812 determines whether the Average PIMDA scoreρ_(avg) is large. In an embodiment, the Average PIMDA score ρ_(avg) isdetermined to be large if it is larger than a predetermined threshold.In another embodiment, the Average PIMDA score ρ_(avg) is determined tobe large if it is substantially larger than a previously determinedAverage PIMDA score.

In response to the process 812 determining that the Average PIMDA scoreρ_(avg) is large, at S828 the process 812 proceeds to S832; otherwisethe process 812 proceeds to S830.

At S830, in response to the process 812 determining that Average PIMDAscore ρ_(avg) is not large, the process 812 reports that PIM-causedinterference was not detected. In an embodiment, at S830 the process 812returns a FALSE result and an empty PIMDA score list.

At S832, the process 812 determines, using all the linear models (localand global), a per-intermodulation-product PIMDA score set {ρ_(I)}respectively corresponding to the data set comprising theper-intermodulation-product Weighted Downlink Power signals WDP_(I) andthe UIM signal (WDP_(I), UIM). Each per-intermodulation-product PIMDAscore ρ_(I) may be determined by the process described with respect toFIG. 9, below. The process 812 then creates a list {I_(High)} of theintermodulation products I having high respective PIMDA scores ρ_(I).The process 812 may determine whether an intermodulation-product PIMDAscore ρ_(I) is high in a similar manner to that described for theAverage PIMDA score ρ_(avg) with respect to S828.

At S836, given a set of PIMDA scores {ρ_(C)} respectively correspondingto different cells, the process 812 may compute a score thresholdρ₀=μ_(ρ)+σ_(ρ), wherein μ_(ρ) is the statistical mean and σ_(ρ) is thestandard deviation of the set of PIMDA scores {ρ_(C)}, to determinewhether the interference detected at a cell C_(f) can be attributed toPIM. In an embodiment, the process 812 determines that interferencedetected at a cell C_(f) is caused by PIM when ρ_(C) _(f) >ρ₀. A PIMDAscore ρ corresponds to the likelihood of PIM being the cause of thedetected interference, with values of ρ closer to 1 indicating a higherlikelihood.

FIG. 9 illustrates process 926 for calculating a PIMDA score accordingto an embodiment. The process 926 may be used in S826 of the process 812of FIG. 8 to produce an Average PIMDA score, or may be used in S832 ofthe process 812 to produce each of the per-intermodulation-product PIMDAscores. The process 926 uses the parameters of a linear modelsdetermined in S710 of process 700 of FIG. 10, such as the slope m ofeach linear model, as well as other parameters (such as the spread σ) ofthe data used to determine the linear models. The linear models mayinclude one global linear model corresponding to a plurality of datasegments and a plurality of local linear models each corresponding to arespective data segment of the plurality of data segments.

To perform the PIM Detection Assessment on the linear model, a set offeatures {F_(i)} characterizing the relation between the WeightedDownlink Power signal WDP and Uplink Interference Metric signal UIM iscomputed, as described below. In the embodiment illustrated, ninefeatures (F1 through F9) are calculated by the process 926, butembodiments are not limited thereto.

At S942, the process 926 determines features related to differencesbetween the local linear models of high-traffic data segments and thelocal linear models of low traffic data segments. The purpose of thesefeatures is to determine how similar is the measured UIM vs. WDPrelation to the expected signature depicted in FIG. 5.

At S942, the process 926 may determine a first feature F1, a Booleanvalue that corresponds to an increase in the slope of the local linearmodels corresponding to high traffic data segments when compared to thelocal linear models for the low traffic segments. The process 926determines an average of the slopes slope_(HIGH) of the local linearmodels for the high traffic segments and an average of the slopesslope_(LOW) of the local linear models for the low traffic segments, andcomputes the value of the first feature F1 as:F1=average(slope_(HIGH))>average(slope_(LOW))  Equation 10The first feature F1 being true indicates that measured interference washigher during times when the power of an intermodulation productassociated with the linear models used to generate the first feature F1would be expected to be higher if PIM was present. Accordingly, thefirst feature F1 being true tends to indicate the presence of PIM.

At S942, the process 926 may also determine a second feature F2, aBoolean value that corresponds to an increase in the spreads for thelocal linear models corresponding to high traffic data segments whencompared to the local linear models for the low traffic segments. Theprocess 926 determines an average of the spreads σ_(HIGH) of the locallinear models for the high traffic segments and an average of thespreads σ_(LOW) of the local linear models for the low traffic segments,and computes the value of the second feature F2 as:F2=average(σ_(HIGH))>average(σ_(LOW))  Equation 10Feature F2 is included to accommodate scenarios where other sources ofinterference, in addition to PIM-based interference, are present. Whenthis is the case, the spread σ represents the deviation of interferencefrom a linear model that is expected in the presence of PIM-basedinterference alone. In some circumstances, it was observed that anincrease in the spread σ was observed in moving from low-trafficconditions to high-traffic conditions, requiring the average overhigh-traffic segments to be greater than the average over low-trafficsegments.

At S944, the process 926 determines features related to thresholds forthe global and local linear models. Examples of the thresholds are shownin Table 4 of FIG. 10.

At S944, the process 926 may determine a third feature F3, a Booleanvalue corresponding to a slope of the global linear model. The thirdfeature F3 is determined according to:F3=m _(G)>ThresSlope_(glob),  Equation 11where m_(G) is the slope of the global linear model andThreshSlope_(glob) is determined as shown in Table 4:ThreshSlope_(glob)=μ_(G)+σ_(G), wherein μ_(G) is the mean and σ_(G) isthe standard deviation of the data used to construct the global linearmodel, and is computed dynamically from the set of analyzed cells.Feature F3 measures how large the slope of the global linear modelassociated with a focus cell is compared to the slope of the globallinear model corresponding to other cells in the network, that is, ittakes into account the rank of the focus cells, in terms of the slope ofthe global linear model, in the network. Hence the comparison with athreshold based on statistics (mean+standard deviation) for the globalslopes.

At S944, the process 926 may also determine a fourth feature F4, aBoolean value corresponding to a slope of the local linear models forthe high traffic data segments. The fourth feature F4 is determinedaccording toF4=average(slope_(HIGH))>ThreshSlope_(high),  Equation 12where ThreshSlope_(high) is determined as shown in Table 4:ThreshSlope_(high)=μ_(hi)+σ_(hi), wherein μ_(hi) is the mean and σ_(hi)is the standard deviation of the data used to construct the local linearmodels of the high traffic data segments, and is computed dynamicallyfrom the set of analyzed cells. Feature F4 captures the behavior of“expected PIM-caused interference”, that is, the plot shown in FIG. 5.For this condition/behavior to be satisfied, the average of the slopesof linear models corresponding to high-traffic segments must be greaterthan a threshold computed from the average slopes during high-trafficconditions of other analyzed cells. Feature 4 differs from Feature 1 inthat Feature 1 compares slopes between linear models of the same cellbut different traffic conditions, while Feature 4 compares slopes forthe same traffic conditions but different cells.

At S944, the process 926 may also determine a fifth feature F5, aBoolean value corresponding to whether a slope of a highest-trafficsegment is greater than a threshold derived from the average slope ofthe high traffic segments:F5=slope[nSegm]>ThreshSlope_(nonDecr),  Equation 13wherein nSegm indicates the data segment with the highest traffic,slope[nSegm] is the slope of the local linear model of the data segmenthaving the highest traffic, and as shown in Table 4,ThreshSlope_(nonDecr)=0.25·average(slope_(HIGH)). Feature F5 takes intoaccount situations where the interference profile “saturates” to a fixedvalue as conditions change from low-traffic conditions to high-trafficconditions. This feature prevents the labeling of interferencemeasurements that show this “saturation” behavior as “PIM.” Duringtesting, saturation behavior was found to be more related totraffic-based interference (from neighboring cells) than to PIM-causedinterference.

At S944, the process 926 may also determine a sixth feature F6, aBoolean value corresponding to a magnitude of a range of UIM signalvalues of a lowest-traffic segment:F6=δUIM[0]<δUIMThres_(low),  Equation 14wherein δUIM[0] is the length of the range of the UIM signal values ofthe lowest-traffic data segment and δUIMThresh_(low) is a predeterminedconstant (e.g., 3 dB) as shown in Table 4. The measurements included inthe UIM signal are already in dBm units, so it is compared withpredetermined threshold expressed in dB. Feature F6 deals with caseswhen interference from other sources (i.e. not PIM) are present duringlow-traffic conditions. Since the aim is to determine how similar theinterference signature is to the plot in FIG. 5, the interference mustbe “flat” (small δUIM) during low-traffic conditions. δUIM[0] is theδUIM of the first (lowest traffic) segment in the data partition.

At S944, the process 926 may also determine a seventh feature F7, aBoolean value corresponding to a magnitude of a range of UIM signalvalues of a highest-traffic segment:F7=δUIM[nSegm]>δUIMThresh_(high),  Equation 15wherein δUIM[nSegm] is the length of the range of the UIM signal valuesof the highest-traffic data segment and δUIMThresh_(high) is apredetermined constant (e.g., 3 dB) as shown in Table 4. Feature F7 isthe flip side of feature F6: a large δUIM must occur during high-trafficconditions for the interference signature to resemble the plot in FIG.5. δUIM[nSegm] is the δUIM of the last (highest traffic) segment in thedata partition.

At S944, the process 926 may also determine an eighth feature F8, aBoolean value corresponding to a magnitude of a floor of UIM signalvalues of a highest-traffic segment:F8=min(UIM[nSegm])>floorUIMThresh_(high),  Equation 16wherein UIM[nSegm] is the set of UIM signal values of thehighest-traffic data segment and floorUIMThresh_(high)=μ_(floor)+1.5 dBas shown in Table 4, where μ_(floor) is the mean, taken over the wholeset of analyzed cells, of the minimum value of the UIM signal restrictedto the last data segment (see the next paragraph). Feature F8 capturesthe requirement for the interference signature to have elevated valuesduring high-traffic conditions.

At S944, the process 926, in determining the threshold values of Table4, computes statistical parameters μ and σ corresponding to the mean andthe standard deviation, respectively, using the full set of cells C_(f)for which, given the intermodulation order n, the set IM_(n)(C_(f)) isnon-empty.

At S946, the process 926 determines a ninth feature F9, a real valuecorresponding to a slope of the highest traffic data segment relative toan expected slope for PIM generated by the given intermodulation orderof the intermodulation product I_(n):F9=slope[nSegm]/I _(n).order,  Equation 17

At S948, the process 926 computes the PIMDA score ρ corresponding to thelikelihood that the measured uplink interference is caused by PIMaccording to:

$\begin{matrix}{\rho = {{\sum\limits_{\underset{Features}{F_{i} \in}}{\gamma_{i}F_{i}\mspace{14mu}{subject}\mspace{14mu}{to}\mspace{14mu}{\Sigma\gamma}_{i}}} = 1}} & {{Equation}\mspace{14mu} 18}\end{matrix}$where each of F1 through F8 is either 0 or 1, F9 is a real number, and{γ_(i)} is set of weights having values chosen to emphasize the featuresassociated with the slope of the linear models for high-trafficsegments, and de-emphasizes the spread of the linear models. Examplevalues for {γ} according to an embodiment are shown in Table 5 of FIG.11.

FIG. 12 illustrates a system 1200 for remote detection of PIM-causedinterference according to an embodiment. The elements of the system 1200may be implemented in, for example, a control system located at the siteof a base station, a performance monitoring system remote from the siteof the base station and communicating with one or more base stations, ora combination thereof.

The system 1200 may store and retrieve data from a Short-Term RetentionDatabase (Short Term DB) 1202 and a Long Term Retention Database (LongTerm DB) 1204. The system 1200 may also obtain data from an OperationsSupport System (OSS) 1206 of a wireless communication network.

The system 1200 operates using an IM Product Generation stage 1208, aData Acquisition stage 1210, a Signal Generation stage 1212, a LinearModel Analysis stage 1214, a PIM Detection Assessment (PIMDA) stage, anda Decision Stage 1218. Each stage of the system 1200 may comprisehardware, software, or combinations thereof, wherein the hardware mayinclude a processor, a memory, an input/output device, or combinationsthereof. In an embodiment, two or more of the stages share hardware,software, or both.

The IM Product Generation stage 1208 may be performed infrequently, suchas when a base station is first activated or a configuration of nearbytransmitters is altered. The IM Product Generation stage 1208 generates,for each cell C in the network and for each nonlinearity order n, theset {IM_(n)(C)} consisting of all intermodulation products of order nwhich overlap with the cell C's uplink channel. Each set {IM_(n)(C)}stored in the Long Term DB 1204.

In an embodiment, the information stored in the Long Term DB 1204 isupdated by performing the IM Product Generation stage 1208 wheneverthere are changes in the configuration (cell plan) of the wirelessnetwork. In an embodiment, the information stored in the Long Term DB1204 includes the information shown in Table 3 of FIG. 6.

The Data Acquisition stage 1210 retrieves uplink interferenceinformation (such as uplink interference counters) of a focus cell C_(f)and respective downlink power information (such as downlink powercounters) of all co-sited source cells C_(S) from the Short Term DB1202, the OSS 1206, or combinations thereof.

The Signal Generation stage 1212 constructs an Uplink InterferenceMeasurement (UIM) signal for one or more nonlinearity orders n and afocus cell C_(f) using the retrieved information corresponding to uplinkinterference of the focus cell C_(f), and constructs a set of WeightedDownlink Power (WDP_(i)) signals from the respective downlink powerinformation of the co-sited cells and the set of potentialPIM-contributing mixing products IM_(n)(C) from the Long Term DB 1204.The analysis is performed on a per-order basis, that is, analysisresults are obtained for nonlinearity of order 2, then a differentanalysis is performed for nonlinearities of order 3, and so on.

The Linear Model Analysis stage 1214 generates, using regressiontechniques, a set of piece-wise linear models (one linear model for eachWDP, signal) for each of the data sets {WDP_(i), UIM}.

The PIM Detection Assessment (PIMDA) stage 1216 computes a PIMDA score ρaccording to key features obtained from the linear models generated bythe Linear Model Analysis stage 1214. A large PIMDA score (i.e., forPIMDA scores having a range of 0 to 1, a PIMDA score ρ near 1) indicatesa higher likelihood that the measured uplink interference was caused byPIM.

The Decision Stage 1218 evaluates a PIMDA score ρ to determine whetherthe PIMDA score ρ is large enough to generate a PIM interference alert.

FIG. 13 illustrates a networked PIM-caused interference detection system1300 according to an embodiment. The system 1300 integrates informationfrom available wireless network sources to detect radio frequencyinterference generated by PIM in the context of a wireless network.Sources of this information, which are hardware elements of a wirelessnetwork, are available in typical wireless cellular networks, but maynot be connected and configured in the manner suggested by thisdisclosure. In particular, a spectrum analytics server 1340 according toan embodiment may be a novel component of a telecommunications network.

A radio access portion of system 1300 may include one or more basestations 1302, each of which are equipped with one or more antennas1304. Each of the antennas 1304 provides wireless communication for userequipment 1308 in one or more cells 1306. As used herein, the term “basestation” refers to a wireless communications station that serves as ahub of a wireless network. For example, in a Long Term Evolution (LTE)cellular network, a base station 1302 may be an eNodeB.

A base station 1302 may correspond to the installation 10 of FIG. 1. Anantenna 1304 may correspond to one of antennas 116A, 116B, or 118 ofFIG. 1.

The base stations 1302 may provide service for macrocells, microcells,picocells, or femtocells 1306. FIG. 13 shows an embodiment in which basestation 1302 provides wireless communication services to three cells1306. The cells may be specific to a particular Radio Access Technology(RAT) such as GSM, UMTS, LTE, NR, etc.

Due to the directionality of some RF antennas 1304, each base station1302 may serve a plurality of cells 1306 arrayed about the base stationsite. In a typical deployment, a base station 1302 provides three to sixcells 1306, which are deployed in a sectorized fashion at a site. Inother embodiments, one or more base station 1302 may be outfitted withan omnidirectional antenna that provides service to a single cell for agiven RAT.

Multiple base stations 1302 may be present at a site and each basestation may support one or more cellular communications technologies(e.g., a base station may support UMTS and LTE cells). The one or moreUE 1308 may include cell phone devices, laptop computers, handheldgaming units, electronic book devices and tablet PCs, and any other typeof common portable wireless computing device that are provided withwireless communications services by a base station 1302.

The system 1300 may include a backhaul portion 1310 that can facilitatedistributed network communications between core elements 1312, 1314 and1316 and one or more base station 1302 within a cellular network. In anembodiment, the backhaul portion of the network includes intermediatelinks between a backbone of the network which is generally wire line,and sub-networks or base stations 1302 located at the periphery of thenetwork. The network connection between any of the base stations 1302and the rest of the world may initiate with a link to the backhaulportion of a provider's communications network. A backhaul 1310 mayinclude an X2 connection through which base stations 1302 communicatewith one another directly.

The core network devices 1312, 1314 and 1316 may be any of a pluralityof network equipment such as a Radio Resource Manager (RRM), a MobilityManagement Entity (MME), a serving gateway (S-GW), a Radio NetworkController (RNC), a base station controller (BSC), a mobile switchingcenter (MSC), a Self-Organizing Network (SON) server, an Evolved ServingMobile Location Server (eSMLC), a Home Subscriber Server (HSS), etc.Persons of skill in the art will recognize that core network devices1312, 1314 and 1316 are different depending on the particular RAT or setof RATs that are present in the network. The core network devicessupport a radio access portion of the network that includes the basestations 1302.

Elements of the communications network 1300 are part of an ElementManagement System (EMS) 1320 and a Performance Monitoring (PM) system1322. The PM system 1322 may include base stations 1306 as well as corenetwork equipment that collect and process performance data andperformance metrics for the network. A spectrum analytics server 1340interfaces with various network components, including components of thePM system 1322 and the EMS 1320.

Although FIG. 13 shows the spectrum analytics server 1340 as a single,discrete component, embodiments are not so limited. For example, inother embodiments, components of the spectrum analytics server 1340 maybe distributed among multiple computing entities. In addition, hardwarefor the spectrum analytics server 1340 may perform processes notdirectly related to detection of PIM-caused interference.

The performance monitoring system 1322 generates performance data 1326for the wireless network. The PM data 1326 may be derived fromobservations of network performance, which may be reported at apredetermined time interval, e.g., every minute, 5 minutes, 15 minutes,hourly or daily. PM data 1326 may include raw event counts (e.g. countsof dropped calls or handover failures during the observation period) orcomplex derived performance indicators (e.g. noise rise normalized byuser loading, Channel Quality Indicator (CQI) distribution statisticsnormalized by data volume, downlink power information, uplinkinterference information, etc.). PM data 1326 may include raw oraggregated performance data.

In some embodiments, PM data 1326 includes data input from a dedicatedPM tool, as well as data received directly from EMS 1320, or elements ofthe Operations and Support System (OSS). In an embodiment, PM data 1326may be derived directly from network event data by the spectrumanalytics server 1340. For example, in an embodiment, when event data1336 is available to the spectrum analytics server 1340, the server mayaggregate individual events to create equivalent PM counters and KeyPerformance Indicators (KPIs). Thus, in some embodiments, PM data 1326is derived from sources other than a PM system 1322.

Fault Management Data 1328 may be transmitted from the PM system 1322 tospectrum analytics server 1340. Fault Management Data 1328 includes, forexample, alarm data that indicates performance issues at one or morecell site.

Configuration Management (CM) data 1330 is input to the spectrumanalytics server 1340 from EMS 1320. CM data 1330 includes the currentconfiguration of various wireless network equipment, such as theconfiguration of base stations 1302 and core components such as RadioNetwork Controllers.

CM Data 1330 is quasi-static and typically only updated as a result ofnetwork optimization such as cell splitting, cell ID reassignment,changes in operating frequency or transmit power, etc. CM data 1330 mayinclude pertinent information such as cell technology (e.g., 2G GSM, 3GUMTS, 4G LTE, 5G NR) associated with physical and logical networkelements, operating frequency, transmit power, reuse codes, type of cell(e.g. macro, micro, pico cell), and other information related to theconfiguration of the radio network elements.

Topology data 1332 is data relating to the location and orientation ofnetwork elements, including information such as the antenna latitude andlongitude of a base station 1302, antenna height, pointing angle forsectorized antennas, antenna beamwidth, site deployment type (e.g.indoor, outdoor, distributed antenna system, etc.), etc. In addition tointerference detection and characterization, topology data 1332 may beused to aid in correlating PM data 1326 and network event data 1336against actual physical locations, and for understanding physicaldistance relationships between network elements.

Network event data 1336 represents discrete network events that aretypically logged by network elements. Network event data 1336 mayinclude information pertaining to the start and termination of phonecalls, information regarding handover of UEs 1308 between network cells1306, measurement reports sent by UEs to network elements, as well asperiodic reporting at intervals of as low as several seconds or lessbetween reporting periods. Network event data 1336 may be available viaa continuous streaming mechanism, or recorded and stored in files atnetwork elements that contain, for example, fifteen to thirty minutes ormore of network event data. Because event data 1336 is reported atintervals of a few seconds, it can be used to determine variance ofconditions over time at relatively short intervals, such as fiveminutes, one minute, 30 seconds, or as low as the reporting interval,which may be less than one second.

Network event data 1336 includes call event data, or cell trace dataaccording to LTE terminology. Call trace data includes informationidentifying makes and models of UEs 1308, and is typically used byoperators to determine device-specific network faults, e.g. that aparticular cell phone model has an unusual rate of handover failuresunder certain conditions. Examples of call event data 1336 includetracking area messages, request for retries, RSSI measurements, andprotocol messages. Network event data 1336 is not conventionally usedfor interference detection, characterization, or localization.

Tools supporting the collection of network event 1336 data may beconfigured to collect selected event types, or to subsample themessaging to a subset of active users. Smaller size network event filesare useful in measuring implied loading on network data transport suchas wireless base station backhaul. When properly configured, networkevents provide high resolution and near real-time information regardingthe operation of targeted network base stations 1302, which can be usedas part of the interference detection processes described by thisdisclosure.

The collection point for network event data 1336 varies between specificwireless technologies and may vary in vendor-specific implementations.For instance, network event data 1336 is typically collected at the RNCentity in 3GPP defined 3G networks (i.e., UMTS, HSPA), but network eventdata 1336 is collected by the eNodeB entity in 4G LTE systems. Networkevent recordings may be pulled directly from the network elements thatstore the events by the spectrum analytics server 1340, or automaticallystored on a separate data storage server, or staging server, such thatexternal systems such as the spectrum analytics server 1340 may accessnetwork event data 1336 without incurring additional data loading on thenetwork elements. Accordingly, it should be understood that networkevent data 1336 may be collected, stored, and retrieved in various waysin different embodiments.

The network event data 1336 may be collected by a trace utility 1334that is integrated with a cellular network. Trace concepts andrequirements are explained, for example, in the Third GenerationPartnership Project (3GPP) Technical Specification TS 32.421.

An embodiment may use network event data 1336. In such an embodiment,PIM interference detection does not use input from a dedicatedPerformance Monitoring system 1322, but may derive base stationperformance indicators directly from network event data 1336. In such anembodiment, network event data records may be aggregated.

Embodiments of this disclosure may utilize additional informationsources beyond the sources illustrated in FIG. 13, such as informationprovided by SON (Self Organizing Network) tools, including analysis andinsight into neighbor relationships not readily apparent from thesources listed above. Additional external integrations may also includeradio frequency propagation planning tools that may be used to enhanceaccuracy of interference detection and interference localization.

The spectrum analytics server 1340 represents a specific processingdevice that interfaces with one or more of the external data sourcesdescribed above. The spectrum analytics server 1340 may perform one ormore of the processes described in this disclosure. In an embodiment,the spectrum analytics server 1340 is physically located in anoperator's Network Operations Center (NOC). From a logical perspective,the spectrum analytics server 1340 is located in the Operations SupportSystem (OSS) plane.

In an embodiment, the spectrum analytics server 1340 may performdetection of PIM-caused interference using one or more processesselected from the set comprising the process 700 of FIG. 7, the process812 of FIG. 8, and the process 926 of FIG. 9.

In an embodiment, the spectrum analytics server 1340 is incorporatedinto a performance monitoring system such as the performance monitoringsystem 140 of FIG. 1.

In an embodiment, the spectrum analytics server 1340 may include theShort Term DB 1202 of FIG. 12, the Long Term DB 1204 of FIG. 12, orboth. In an embodiment, the spectrum analytics server 1340 may beconfigured to perform of or more of the stages 1208, 1210, 1212, 1214,1216, and 128 of the system 1200 of disclosed in FIG. 12.

In an embodiment, the spectrum analytics server 1340 produced PIManalysis data 1338 that may be archived or reported supervisorypersonnel. The PIM analysis data 1338 may include one or more PIMDAscores, one or more factors used in generating PIMDA scores, or both,among other information.

In an embodiment, the spectrum analytics server 1340 may produce a PIMDetection Output 1342 on an output device in response to determiningthat an amount of PIM-caused interference is more than an allowedamount, in order to notify supervisory personnel of a potential need toperform maintenance or repair activity at a base station.

Many modern wireless networks utilize shared cells sites where two ormore network operators are co-located at the same physical cell sitepremises and typically share the same cell tower to mount theirantennas. In these shared cell site deployments, PIM products may begenerated by either the equipment owned and maintained by an operatorexperiencing PIM related problems, or the root cause of the interferencemay be transmitters owned and operated by other network operatorssharing the common cell site. In many cases a combination oftransmitting equipment spanning two or more operators leads to PIMproducts that impact one or more wireless receivers, and transmissionsfrom one operator can cause PIM interference that affects a receiver ofa different operator.

FIG. 14 illustrates a situation in which transmissions from a firstoperator cause interference to a second operator. Line 1402 represents apoint at which frequencies are divided between the first and secondoperators. Frequencies that are below frequency 1402 are licensed to afirst operator, which frequencies above frequency 1404 are licensed to asecond operator. Although FIG. 14 illustrates an embodiment in which thefrequencies for two operators are adjacent to one another, it is commonfor the licensed frequencies of two co-sited operators to be separatedby a gap.

When Operator 2 transmits downlink signals DL1 and DL2, thosetransmissions cause PIM third order products P3 and fifth order productsP5. While some of the products are in frequencies licensed to the secondoperator, other products are in frequencies licensed to the firstoperator. Accordingly, FIG. 14 illustrates a scenario in whichtransmissions from one operator cause interference to frequencies thatare licensed to another operator. In other examples, downlinktransmissions from different operators combine to create PIM productsthat affect at least one of the operators.

It is difficult to detect PIM products in TDD systems using a cellularantenna since the cellular antenna is only transmitting or receiving thesame frequencies at any given point in time. However, transmissions froman operator that uses a TDD system can cause PIM products that affectanother operator's co-sited equipment. For example, transmissions from aTDD system can cause PIM products in frequencies dedicated to receptionby another operator that operates an FDD system. In another scenario, afirst operator's TDD transmissions are out of synchronization with TDDsystems of other co-sited operators, so that transmissions from thefirst operator during a transmission mode cause PIM products that aredetected by the second operator during its reception mode.

Embodiments are directed to a system and method for sharing data betweendifferent operators that operate co-sited cellular base stations. Inconventional systems, network performance data, and to some extentnetwork configuration data, is not shared between network operationsteams. Network performance data is often treated as a trade secret as itmay reveal strengths and weaknesses of an operator's network.Performance data such as excessive call failures or statistically lowdata throughput rates that could be leveraged by competitors.

In an embodiment of the present disclosure, detection of interference inshared cell site deployments may utilize configuration and real-timenetwork performance data from all equipment housed at the shared site todetect the presence of interference external to the network. However,network performance data, and to some extent network configuration data,is not generally shared between different network operations teams thatare typically in commercial competition with one another.

External interference refers to interference that is not caused byregularly scheduled transmissions by an operator. An operator's regularscheduled transmissions, such as normal uplink and downlinktransmissions between a base station and UE, are internal interference,which is handled by pre-existing processes such as inter cellinterference coordination (ICIC). In contrast, external interference iscommonly caused by foreign sources such as a sparking transformer and arogue transmitter. PIM interference is a form of external interference.

FIG. 15 illustrates an embodiment of a process for detecting externalinterference using data from at least two operators. The process mayinitiate at S1502 by receiving network configuration data at a securemodule. The network configuration data may be received on systeminitialization, and updated when changes are made to the networkconfiguration.

In an embodiment, network configuration data describes the physicalarrangement and radio frequency channel provisioning of the wirelessnetwork. For example, network configuration data may include networktopology information such as the geographical location of cell sites,the antenna pointing angles of all cells within a given cell site, thetechnology utilized by each cell site transceiver (e.g. GSM, UMTS, LTE,etc.) and the specific frequency channels used by each cell fortransmission and reception along with associated information such asoccupied channel bandwidth (e.g. 10 MHz, vs 20 MHz LTE).

FIG. 16 illustrates a first embodiment of a system for using data frommultiple network operators to determine the presence of externalinterference in a wireless communications network. In the system of FIG.16, an operator exchanges information with another operator andindependently performs interference detection. Only two operators areillustrated in FIG. 16 for the sake of simplicity, but the number ofoperators that can cooperate to detect external interference is notlimited.

In the first embodiment, Interference Detection A, which is aninterference detection module controlled by Operator A, receives networkconfiguration data for operator A's system. The network configurationdata may be received from an internal system such as a configurationmanagement server or input manually by the operator.

In addition, Operator A's interference detection module receives networkconfiguration data from Operator B. The data received from competingoperators is protected so that it cannot be disseminated by any otheroperator. For example, exchanged data may be encrypted using anencryption key that is not available to any competing operators. In suchan embodiment, the decryption key is retained by the interferencedetection so that it is not available to public scrutiny. In anembodiment, at least a portion of a decryption key has a hardwarecomponent, e.g. a particular circuit on a field programmable gate array(FPGA), an application specific integrated circuit (ASIC), or a plug-indevice such as a memory device that interfaces with an interferencedetection module through a universal serial bus (USB).

In an embodiment, the interference detection modules are programmedduring installation to carry the correct decryption keys such that theycan utilize the network data from cell sharing partners, but this datais not available for human inspection or display without the consent ofthe cell sharing partner.

The first embodiment of FIG. 16 is not limited to using any particularinterference detection module for any operator. In some embodiments,each sharing partner uses their own interference detection technology,and that interference detection technology benefits from operationalinformation from multiple operators.

A second embodiment of a system for using data from multiple networkoperators to determine the presence of external interference in awireless communications network is shown in FIG. 17. In the secondembodiment, two or more operators provide encrypted network data to acentral computer that is not controlled by any of the operators.

Operators may be reluctant to provide proprietary data to a competingoperator, so the interference detection module may be provided by and/orcontrolled by a third party. In some embodiments, computing hardwarethat runs an interference detection module is provided by the thirdparty. The hardware component may be in a location that is not under theexclusive control of either operator, such as the cell site for theco-sited operations that are being analyzed to detect interference.

In an embodiment, a central interference detection module is hosted by athird party that provides remote computing services. For example, thecentral interference detection module may be administered by a cloudcomputing service provider, and encrypted network information may beprovided to the module over the Internet. In some embodiments,information is conveyed using one or more secure transport technology.

The centralized PIM detection module may be pre-provisioned with eachparticipating operator's encryption keys, and be capable of ingestingand analyzing the network data to identify external interference. Eachoperator may access the shared PIM detection device and view:

-   -   PIM detection results that do not expose raw network        configuration or performance data. This output data from the        interference detection module may be available in a common        format to all operators supported by the device per licensing        agreements.    -   Network performance data unique to the operator in question and        accessed via unique operator log in codes (e.g. the same or        similar codes used to originally encrypt the proprietary data).        This common processing approach can be extended to multiple        operators in an interference detection system.

After the network configuration data from S1502 is received, it is usedto determine parameters for detecting interference at S1504. Forexample, when detecting PIM interference, the interference detectionmodule uses the network configuration data to determine whichfrequencies are used by each operator for uplink and downlinktransmissions. When determining whether PIM interference is present,these frequencies can be used to determine which transmissions areexpected to generate PIM interference, and which frequencies to analyzefor evidence of intermodulation products. In other embodiments, uplinkfrequencies may be catalogued to support identifying interferencepatterns.

Network performance data and event data is received by the interferencedetection system at S1506. The network performance data and event datamay be received by the system on an ongoing basis at regular reportingintervals, and used to detect sub-performing cells and to direct furtheranalysis for potential external interference.

Managed wireless network equipment typically provides periodicperformance data reports depicting overall network performance over timeperiods on the order of 15 minutes to an hour or more. Over eachreporting period information such as signal strengths, dropped calltallies, transmit and received data rates for each network cell arereported. The performance data may be transmitted by each operator to aninterference detection system on an ongoing basis as it is collected.

Network event data may be high resolution time-series data provided bynetwork cells. Network event data may be collected at a resolution of 1minute to several seconds, and may be received by an interferencedetection system according to the reporting interval, or on some otherperiodic basis. Network event data may include non-periodic events suchas information regarding the arrival of new calls, or network events asthey happen such as dropped calls or failed handovers.

Network event data is utilized by some interference detection modules toinvestigate interference events on finer time scales than is possiblevia the summary network performance data described above.

The data collected by an interference detection module is analyzed todetermine whether external interference is present at S1508. For thedetection of PIM interference, analyzing the data may include detectinginterference products as described above in the present disclosure.However, embodiments are not limited to PIM detection alone. In otherembodiments, the presence of other types of interference may bedetected.

For example, interference may extend across a portion of spectrum thatextends beyond the uplink frequencies of one operator for a particularcell. In such a case, an interference detection module can analyze arelatively broad spectrum that includes frequencies of all operatorsthat contribute data to the detection module. In some embodiments, theshared data can be used to increase the resolution of an interferencelocalization process.

In an embodiment, UE reports may be used to pinpoint a source ofbroadband interference. Such an embodiment could utilize data from UEreports from multiple operators to increase the accuracy of alocalization process. Similarly, the system described by this disclosuremay be used to improve the results of network processes besidesinterference detection.

Interference detected by the detection module may be resolved by anoperator at S1510. Examples of resolving interference may include one ormore of:

-   -   deploying personnel to identify and resolve a cause of the        interference,    -   characterizing interference by one or more of time, frequency        and location in a report provided to an operator,    -   adapting parameters of one or more cellular antenna to reduce        the effects of interference on the network,    -   contacting an entity in control of a source of interference,        e.g. a sparking transformer, to engage the controlling entity in        an effort to eliminate the source of interference, and    -   determining a probable source of interference, e.g. a rogue        transmitter or radar installation, and providing the probable        source to an operator.

PIM interference is typically caused by a faulty connector in an RFsignal path or by reflections off of a corroded surface, so resolvingPIM interference may include repairing an RF cable connection in asignal path of a receiver, removing oxidation from a component of thereceiver, or reducing the effect of a nonlinearity that causes PIMproduct reflections. The effect of the nonlinearity can be reduced, forexample, by removing corrosion or oxidation from a surface or applyingshielding to the surface.

In some embodiments, the accuracy of PIM interference detection can beenhanced by performing additional processes to characterize interferencedetected by a cellular network. Interference that is detected by thenetwork may be analyzed to determine whether the interference hascharacteristics suggesting that it is not associated with PIM.

For example, an initial data analysis cycle may be carried out to detectinterference events that are observed by multiple geographicallyseparated cell sites with a high level of correlation to the time seriesmagnitude of the detected interference event.

As seen in FIG. 18, a PIM detection system may be configured in aprocess 1800 that includes determining parameters of cellular receiversat S1802. Some of the parameters that may be determined here are alocation of a receiver, e.g. a set of geographic coordinates, a type orphysical configuration of the receiver, and which channels, orfrequencies, the receiver is configured to receive in. The parametersmay be retrieved from a configuration management server or inputmanually.

Cellular networks experience PIM interference in two primary forms. Thefirst form is caused by nonlinearities in physical cellular equipment,such as a poor contact junction (e.g. junctions 114A and 114B of FIG. 1)or a corroded ground strap. This form of PIM may be referred to asconductive PIM.

There are several different varieties of cellular receivers. eNodeBmacro cells typically have a single physical receiver service to a cell,and three receivers are typically mounted on a single cell tower. Inenclosed spaces such as a tunnel in a highway and building interiors, areceiver may be in the form of a leaky cable that extends over a longdistance. In areas such as stadiums and large arenas, a single cell maybe associated with a distributed network of receive antennas that sharea common signal path. For purposes of the present disclosure, a receiverrefers to the physical structure that receives cellular RF transmissionsand shares an RF signal path for one or more channel of a cell, even ifmultiple receive antennas are involved.

The second form of PIM interference occurs when radio waves from atransmitter reflect off a nonlinearity, e.g. the corroded area 120 c ofFIG. 1. This form of PIM may be referred to as radiative PIM. Whileradiative PIM can affect a plurality of receivers in proximity to thereflections, conductive PIM generally affects a single receiver.However, signal strength of the reflections is relatively low, and mayfall below the noise floor after travelling about 10 meters.

For each receiver in a network, nearby neighbors may be determined atS1804. Here, the nearby neighbors are neighbors that could be affectedby the same radiative PIM as a target receiver. Accordingly, the nearneighbors may be all receivers within a predetermined distance of thetarget receiver, e.g. all receivers within 20 meters. Because radiativePIM rapidly decays, PIM artifacts are not expected to affect receiversthat are more than 10 meters from a nonlinearity that causes PIMinterference, so a distance of 20 meters accounts for a case in whichtwo receivers are equidistantly separated from a reflectingnonlinearity. In some embodiments, other distance values may be used todetermine near neighbors, such as 15 meters and 10 meters.

Far neighbors of a target receiver may be determined at S1806.Determining far neighbors may include determining receivers that arelocated at multiple tiers of distance, e.g. receivers that are withinone kilometer and receivers that are within 10 kilometers. These groupsmay be used to determine whether interference patterns are present overa large area—if interference is highly correlated for all receivers in a10 kilometer radius, the interference is not likely to be associatedwith PIM. Far neighbors may be all receivers that are not nearneighbors. In specific embodiments, far neighbors may be receivers thatare separated by a minimum distance of 10 meters, 20 meters, 100 meters,etc.

In some embodiments, neighbors of a target receiver are receivers thatreceive on at least one frequency or channel as the target receiver.However, receivers that receive in other channels may be considered asneighbors, e.g. to determine whether broadband interference is present.In some embodiments, all neighbor receivers that are not classified asnear neighbors are classified as far neighbors, and a system maydetermine individual distances from a target receiver to each respectivefar neighbor. Information gathered by process 1800 is recorded in a PIMdetection system at S1808, e.g. by recording associated data in acomputer memory.

Because receiver parameters tend to remain constant for extended periodsof time, process 1800 may be performed once when a PIM detection systemis established, and then elements of the process may be updated whenreceiver parameters change. In other embodiments, process 1800 may beperformed on a periodic basis, or when new equipment is installed in anetwork.

After a PIM detection system has been configured by process 1800, it maybe used to determine whether PIM interference is present in a cellularnetwork in a process 1900.

Interference is detected at S1902. In cellular networks, interferencemeasurements are made automatically at predetermined intervals andreported in performance metrics such as a signal to interference ratio(SIR), a signal to interference plus noise ratio, (SINR),carrier-to-noise ratio (CIR), etc. Accordingly, an embodiment mayinclude capturing interference-related metrics for respective receiversfrom a network's performance monitoring (PM) system. Embodiments maycapture PM data, Event data, and Alarm data as interference data todetermine whether interference is affecting a receiver.

Interference detected by receivers may be correlated at S1904 todetermine whether interference detected at multiple receivers correlatesin one or more of time, frequency and magnitude. In an embodiment, thecorrelations may be categorized according to a probability that they areassociated with PIM.

In an embodiment, correlations are performed for receivers that areseparated by a distance at which radiative PIM would not be detected,e.g. receivers that are determined to be far neighbors at S1806.Interference that is highly correlated between far neighbors is notlikely to be associated with reflective PIM. Similarly, interferencethat is detected by a single receiver and is not correlated to any ofthe receiver's nearby neighbors is suggestive of conductive PIMinterference, while interference that is highly correlated to closeneighbor receivers but not correlated to far neighbor receivers mayindicate radiative PIM interference. Therefore, multiple correlationsmay be performed between a target cell and its near and far neighbors atS1904.

In an embodiment, an interference detection system records one or moreset of correlated receivers from S1904 in one or more multi-receiverevent list 1920. In one embodiment, sets of receivers with interferencethat is correlated in dimensions such as time and frequency are combinedinto a single event list 1920 based on the strength of correlations. Forexample, correlations that are higher than a threshold value may beplaced on the list 1920. The threshold value may be, for example, 0.50,0.75, 0.90, etc. If the only receivers in a set of correlated receiversare near neighbors, then those receivers may not be designated as a setof correlated receivers in the event list 1920.

Each of the detected multi-site events may be broken down into shortduration time intervals to create time-series activity data 1930 atS1906. The time intervals may be limited by the time resolution of theinterference detection processes of a network. For example, the timeintervals may have 15 minute time resolution for detection based on 15minute network performance data or 1 minute time resolution fordetection based on 1 minute network event data.

Receivers may be grouped for each correlation that suggests a source ofinterference other than PIM. An example of these groupings can be seenin list 1920, which show two groups of receivers (ABC and XYZ) that areeach correlated with a different interference event.

An activity flag may be set for each time interval for the correlatedgroups. FIG. 19 shows an embodiment of activity data 1930 with a binaryactivity indicator, which may indicate whether an event that establisheda correlated group has been detected at a level that exceeds apredetermined threshold for each time slot. The threshold may be basedon one or more factors chosen from the magnitude of interference, anumber of interference measurements in which interference was detectedwithin a time period, a correlation value between one or more of thecells, etc.

Although activity data 1930 in the figure only shows binary activitydata, or data that is weighted at 1 or 0, other embodiments arepossible. Interference detection and determination can be affected by anumber of variables, and it is not always clear whether interference isdefinitively caused by something other than PIM. Even when interferencethat is not caused by PIM is detected at a receiver, the receiver maystill be experiencing interference caused by PIM in addition to theother source of interference. Therefore, in some embodiments, aweighting value is assigned to time slots in which correlatedinterference is present for each correlated group at S1908. Theweighting value may be a value between 0 and 1, and may be applied, e.g.to the PIMDA score ρ discussed above for each respective time slot. Suchan embodiment may identify PIM interference at a receiver with a highPIDMA score even when the receiver is being affected by interferencethat is correlated with multiple receivers.

Persons of skill in the art will understand that various weightingschemes are possible. For example, a weighting value may be applied to agroup of receivers based on one or both of a frequency correlation scoreand a time correlation score. A non-exhaustive list of other values thatcould affect a weighting score includes a number of receivers in thegroup, a distance between receivers, e.g. a maximum, median or meandistance, a number of samples in a time period in which correlatedinterference is detected, traffic levels at one or more of thereceivers, an environment in which receivers are located (e.g.industrial, urban or rural), a time of day, etc. Examples of trafficlevels

PIM detection may be performed for one or more receiver at S1910 inaccordance with embodiments of the present disclosure.

PIM interference is a localized phenomenon impacting specific receiversthat are in close proximity to powerful transmitters. Thus, interferenceevents that are highly correlated between distant receivers cannot havePIM as their root cause.

The ability to detect true PIM events is enhanced by eliminating orreducing the weight given to time slots during which other types ofinterference is present. Therefore, the weighting from S1908 is appliedto time slots in which correlated interference is detected whendetermining whether PIM interference is present at receivers in acorrelated group at S1910.

FIG. 20 illustrates another embodiment of a process 2000 that candetermine whether PIM interference is present in a cellular network.While process 1900 determines correlations and uses interference that iscorrelated between multiple receivers that are separated in space toimprove PIM interference detection, process 2000 uses interferencecharacteristics that can be detected at a target receiver. Process 2000can be practiced in conjunction with or separate from process 1900.

Interference is detected at a target receiver at S2002, andcharacteristics of the interference are determined at S2004. Inparticular, the process may determine whether characteristics ofinterference detected at a target receiver suggest that the detectedinterference is not caused by PIM. There are several categories of thesecharacteristics.

One such category is wideband interference that simultaneously affectsmultiple wideband RF channels. PIM interference which results fromtransmit mixing products is usually of narrower bandwidth thaninterference types such as industrial RFI generated by faulty machineryis often broadband, potentially spanning hundreds of MHz or more of RFspectrum. Time slots during which this type of interference is detectedmay be weighted less strongly or ignored completely when determiningwhether a receiver is affected by PIM interference.

Interference detected during times of low base station transmit activitymay be similarly de-weighted or excluded during PIM detection. PIMinvolves mixing products of relatively high power transmit signals thatfall into receiver RF channels. During times with low to no transmitactivity, the likelihood of PIM is reduced or eliminated. Someinterference types, such as constantly elevated receiver noise floorsthat do not correlate with transmitter activity, may be de-weighted orignored for the purpose of detecting PIM events.

Another example of a characteristic that suggests interference is notcaused by PIM is periodicity. Some detected interference eventsdemonstrate highly stable repetitive nature such as interference fromswept radar systems. PIM interference typically follows downlinktransmitter activity. If transmitter activity in the vicinity of thecell has some periodicity (typically transmitter activity has a 24 hourperiodicity or weekly periodicity), interference with periodicity notmatching traffic periodicity is unlikely to be associated with PIM. Iftransmitter activity is not periodic, interference with high periodicityis unlikely to be associated with PIM. Therefore, in an embodiment, asystem determines whether interference is detected in a periodic patternat S2004. Persons of skill in the art will understand that varioustechniques for determining periodicity such as autocorrelation can beused to determine a periodicity characteristic.

Time-series activity data is created for time slots based on the non-PIMcharacteristics at S2006. In an embodiment, a time slot is flagged for areceiver if non-PIM activity was detected at S2004 in that time slot.

A weighting is applied to the flagged time slot at S2008. The weightingmay be a binary multiplier or some other weighting value that is scaledto one or more of an amount of activity in a time slot, a degree ofconfidence that detected interference is not caused by PIM, a type ofthe detected interference, etc.

In one embodiment, a method for determining interference that isexternal to cellular telecommunications in a secure module that preventsa first operator that operates a first network from accessing networkconfiguration and performance data of a second operator that operates asecond network includes receiving network configuration data and networkperformance data for a first cell operated by a first operator, theperformance data including interference measurements for frequencieslicensed to the first operator, receiving network configuration data andnetwork performance data for a second cell operated by a second operatorand co-sited with the first cell, the network performance data includinginterference measurements for frequencies licensed to the secondoperator, and identifying interference external to the cellulartelecommunications system that affects the first cell and the secondcell using the network performance data from the first and second cells.

The method may further include receiving downlink power information of aplurality of transmitters, determining intermodulation productinformation for the plurality of transmitters, and using the downlinkpower information and the intermodulation product information todetermine interference generated by passive intermodulation (PIM).

Determining the interference generated by PIM may include determining,using the intermodulation product information and the downlink powerinformation, a Weighted Downlink Power (WDP) signal for anintermodulation product, the WDP signal including a plurality ofexpected power values, and determining, using the WDP signal, a PIMDetection Assessment (PIMDA) score of the intermodulation product,wherein a value of the PIMDA score corresponds to interference generatedby PIM.

In an embodiment, the intermodulation product information includesinformation for intermodulation products that occur in uplinkfrequencies allocated to both of the first and second operators.

In an embodiment, the downlink power information includes informationfor downlink frequencies allocated to both of the first and secondoperators.

In an embodiment, at least one of the first cell and the second celltransmit and receive signals using time division duplexing (TDD).

In an embodiment, both of the first and second networks are TDDnetworks, and a time that is allocated for transmitting in the firstnetwork is allocated to receiving in the second network.

In an embodiment, determining the PIMDA score includes generating, usingthe WDP signal and the uplink interference information, a plurality oflinear models for the intermodulation products, determining, using thelinear models, a plurality of features of the intermodulation product,and determining, using the plurality of features, the PIMDA score,wherein a value of the PIMDA score corresponds to interference generatedby PIM and received by the receiver. Generating the plurality of linearmodels may include generating a global linear model expressing arelationship between all of the expected power values of the WDP signaland the corresponding received interference values of the uplinkinterference information, partitioning the expected power values of theWDP signal into a plurality of segments, and generating a local linearmodel for each of the segments, the local linear model expressingrelationships between all of the expected power values of the segmentand the corresponding received interference values of the uplinkinterference information, wherein the segments are disjointed from eachother.

An embodiment of the method further comprises determining first downlinkand uplink frequencies for the first operator from the networkconfiguration data, determining second downlink and uplink frequenciesfor the second operator from the network configuration data, anddetermining PIM products for downlink transmissions in both of the firstand second uplink frequencies.

This document describes a system and a process for remote detection ofPIM-caused interference in wireless networks. Embodiments of thisdisclosure allow operators to automatically detect the presence of PIMinterference in a wireless communications network. An operator canimplement embodiments to easily identify cells being impacted by PIMwithout resorting to service interruptions. The PIM Interference RemoteDetection system may have of a long-term retention database where a setof intermodulation products is stored, and a short-term retentiondatabase in which uplink interference and downlink transmission powermetrics are stored for each cell of a plurality of monitorable cellsoperating in a vicinity. The method then uses the information from thesedatabases to compute a PIM Detection Assessment score that indicates thelikelihood that uplink interference measurements can be attributed toPIM.

An operator can use information from embodiments of this disclosure todeploy personnel to remedy the physical cause of interference, such asreplacing an oxidized connector or notifying a power company of amalfunctioning component. An operator may be a licensor of RF spectrumthat operates a cellular telecommunications network.

Embodiments of the present disclosure represent improvements to cellulartelecommunication technology. Embodiments can analyze and characterizeinterference without requiring network service interruptions, andwithout installing additional signal generation or energy sensingequipment in network areas.

Embodiments of the present application are directed to a novel methodfor detecting interference caused by PIM in cellular telecommunicationnetworks. For example, the present disclosure provides a method by whichPIM interference that is caused by transmissions in a first channel canbe detected by its effects on a second channel in a different frequencyfrom the first channel. The effects can be detected remotely by one ormore computing device that interfaces with existing network equipment,thereby representing a substantial improvement over technologies thatrequire direct measurements of signals by a portable device. Embodimentsmay detect interference caused by or affecting a TDD cellular system byleveraging TDD systems of co-sited cells that are out of sequence withone another, e.g. one cell is transmitting while the other cell isreceiving. An embodiment may include adjusting timing of at least onecell to ensure that transmissions and receptions are adapted to detectinterference. PIM detection may be further improved by excluding orde-weighting time slots in which a receiver experiences externalinterference that is not PIM, e.g. interference that is highlycorrelated across multiple distant neighbors.

Using data from multiple operators to enhance efforts for externalinterference detection and other automated network processes is animprovement to existing technology as well. Conventional systems onlyuse data from a single operator, so they cannot detect, for example,intermodulation products that appear in frequencies allocated to adifferent operator. Commercially competing operators do notconventionally share network data, so this and other improvementsassociated with a broader set of data are improvements to existingtechnologies.

What is claimed is:
 1. A method of automatically detecting passiveintermodulation (PIM) interference at a target receiver in a cellularnetwork, the method comprising: receiving interference data over a timeperiod for the target receiver; determining whether the receivedinterference data has at least one non-PIM characteristic; applying aweighting factor to at least one time slot of the time period in whichinterference with the non-PIM characteristic is detected; determiningwhether PIM interference is present at the target receiver by analyzinginterference at the target receiver over the time period with the atleast one weighted time slot; receiving interference data over the timeperiod for the plurality of receivers; determining correlations betweeninterference detected by the plurality of receivers and the targetreceiver in the time period; determining a second time slot in the timeperiod in which the correlations exceed a predetermined value; anddetermining a second weighting factor based on the correlations; whereindetermining whether PIM interference is present in the second time slotincludes applying the second weighting factor to the second time slot.2. The method of claim 1, wherein the at least one non-PIMcharacteristic is selected from a broadband interference characteristic,a low traffic level for one or more transmitter within 20 meters of thetarget receiver, and a periodicity characteristic.
 3. The method ofclaim 1, wherein the weighting factor is chosen from 1 and 0 based on alevel of at least one of the correlations, and a weighting factor of 0excludes an associated time slot from consideration when determiningwhether PIM interference is present at the target receiver.
 4. Themethod of claim 1, wherein determining whether PIM interference ispresent comprises: determining intermodulation product information for aplurality of transmitters; receiving downlink power information of theplurality of transmitters; determining, using the intermodulationproduct information and the downlink power information, a WeightedDownlink Power (WDP) signal for an intermodulation product, the WDPsignal including a plurality of expected power values; and determining,using the WDP signal, a PIM Detection Assessment (PIMDA) score of theintermodulation product, wherein a value of the PIMDA score correspondsto interference generated by PIM.
 5. The method of claim 1, furthercomprising: determining a plurality of receivers in the cellular networkthat are separated from the target receiver by a minimum distance. 6.The method of claim 1, wherein the correlations are based on at leastone of periodicity, frequency, and magnitude of interference.
 7. Themethod of claim 1, further comprising: resolving the interference byrepairing an RF cable connection in a signal path of the receiver,removing oxidation from a component of the receiver, or reducing theeffect of a nonlinearity that causes PIM product reflections.
 8. Asystem for automatically detecting passive intermodulation (PIM)interference at a target receiver in a cellular network, the systemcomprising: one or more processors; and memory storing instructionsthat, when executed by the one or more processors, cause the system to:receive interference data over a time period for the target receiver;determine whether the received interference data has at least onenon-PIM characteristic; apply a weighting factor to at least one timeslot of the time period in which interference with the non-PIMcharacteristic is detected; and determine whether PIM interference ispresent at the target receiver by analyzing interference at the targetreceiver over the time period with the at least one weighted time slot;receive interference data over the time period for the plurality ofreceivers; determine correlations between interference detected by theplurality of receivers and the target receiver in the time period;determine a second time slot in the time period in which thecorrelations exceed a predetermined value; and determine a secondweighting factor based on the correlations, wherein determining whetherPIM interference is present in the second time slot includes applyingthe second weighting factor to the second time slot.
 9. The system ofclaim 8, wherein the at least one non-PIM characteristic is selectedfrom a broadband interference characteristic, a low traffic level forone or more transmitter within 20 meters of the target receiver, and aperiodicity characteristic.
 10. The system of claim 8, wherein theweighting factor is chosen from 1 and 0 based on a level of at least oneof the correlations, and a weighting factor of 0 excludes an associatedtime slot from consideration when determining whether PIM interferenceis present at the target receiver.
 11. The system of claim 8, whereindetermining whether PIM interference is present comprises: determiningintermodulation product information for a plurality of transmitters;receiving downlink power information of the plurality of transmitters;determining, using the intermodulation product information and thedownlink power information, a Weighted Downlink Power (WDP) signal foran intermodulation product, the WDP signal including a plurality ofexpected power values; and determining, using the WDP signal, a PIMDetection Assessment (PIMDA) score of the intermodulation product,wherein a value of the PIMDA score corresponds to interference generatedby PIM.
 12. The system of claim 8, wherein the instructions executed bythe one or more processors further cause the system to: determine aplurality of receivers in the cellular network that are separated fromthe target receiver by a minimum distance.
 13. The system of claim 8,wherein the correlations are based on at least one of periodicity,frequency, and magnitude of interference.